Design Optimization for Large High-Force Soft Robot Manipulators Under Gravitational Loads

Isara Cholaseuk; Penelope Llibre; Alexa Kyriacou; Audrey Wang; Akua K. Dickson; Ran Jing; Juan C. Pacheco Garcia; Andrew P. Sabelhaus

cs.ROAug 17, 2026

Design Optimization for Large High-Force Soft Robot Manipulators Under Gravitational Loads

Authors: Isara Cholaseuk, Penelope Llibre, Alexa Kyriacou, Audrey Wang, Akua K. Dickson, Ran Jing, Juan C. Pacheco Garcia, Andrew P. Sabelhaus

Organizations: Department of Mechanical Engineering, Boston University, Boston MA, USA · Division of Systems Engineering, Boston University, Boston MA, USA

Abstract

Designing large soft robots capable of generating high forces for physical human-robot interaction remains a significant challenge in soft robotics. Prior work in large soft robots has focused on proof-of-concept prototypes, and no systematic framework exists for determining the suitability of a design paradigm for a desired task. This manuscript introduces a method for optimizing the geometry of a soft robot limb, maximizing its blocking force subject to an anti-bucking constraint under its own gravitational loading. We demonstrate that an explicit solution exists to the proposed optimization problem under certain assumptions. Experiments with three geometries of a large, soft, pneumatically-actuated manipulator demonstrate that the method correctly predicts which designs meet constraints and which produces the largest end-effector forces. This method, with its closed-form solution, can allow designers to determine a-priori if an intended class of soft manipulators is an appropriate choice for physical interaction at large size scales.

Figures & tables

Fig. 1: We propose an optimization framework for the geometry of a soft limb that maximizes the blocking force subject to a fixed amount of material and anti-buckling conditions. Hardware tests validate the anti-buckling constraint (yellow) as well as optimality for blocking force (pink vs. purple).

Fig. 2: Problem setup of an arbitrary soft robotic limb with complex geometrical shape. a) Arbitrary geometrical design parameters of the soft limb. b) Actuated limb interacting with external blocking force. c) Limb’s distributed weight under gravity.

Fig. 3: Design parameters of the prototype PneuNet soft manipulator. (a) 1D PneuNet. (b) Prototype limb made of three PneuNets, with the bottom side oriented on the left. (c) Cross-section at $I_{min}$ in hatched area. (d) Cross-section at $I_{max}$ in hatched area. (e) Cross-section at the connection in hatched area.

Quantities	Variables	Values	Units
Fixed Variables	$l$	$0.014$	m
	$h$	$0.018$	m
	$t$	$0.006$	m
Design Requirements	$\bar{L}$	$0.49$	m
	$\bar{V}$	$8.387e^{-4}$	m 3
Material Properties [ 35 ]	$E_{sil}$	$1.793$	MPa

TABLE I: Numerical Values Used for Optimization Analysis

Fig. 4: Manufacturing steps. (a) Mold casting chambers. (b) Overmolding seal for PneuNet. (c) Overmolding connection for multiple PneuNets to become one manipulator limb. (d) Final limb after removing supports.

Fig. 5: Buckling test setup: a) Limb buckled at maximum length, b) Limb deflected by $\delta$ due to self-buckling with lower-length, c) Limb unbuckled at length $L$ .

Fig. 6: Blocking force test setup of limb, with (a) $w_{30}$ , (b) $w_{40}$ , (c) $w_{45}$ .

Fig. 7: Empirical $\delta$ at varying $L$ of each limb where the cross shows empirical $\hat{L}_{max}$ and the dashed lines represent ${L}_{max}$ calculated from equation ( 6 ). $\Delta L_{max}$ is visualized for $w_{30}$ as an example.

Limb	$w_{30}$	$w_{40}$	$w_{45}$
$L_{max}\ \text{m}$	$0.438$	$0.515$	$0.551$
$\hat{L}_{max}\ (\pm 0.007\ \text{m})$	$0.447$	$0.517$	$0.517$
$\Delta L_{max}\ (\text{m})$	$0.009$	$0.002$	$0.035$

TABLE II: Approximated and empirical buckling length of each limb

Fig. 8: The data demonstrates force optimality of $w_{45}$ limb since all $F_{45}(\Delta u)>F_{40}(\Delta u)>F_{30}(\Delta u)$ for all $\Delta u>\Delta u_{crit}$ .

Soft Robotics

Soft robotics studies robots with compliant bodies and actuators that deform to move, sense, and interact with uncertain environments.

See all 231 papers

A Unified Low-Dimensional Design Embedding for Joint Optimization of Shape, Material, and Actuation in Soft Robots
Mar 6, 2026Vittorio Candiello, Manuel Mekkattu, Mike Y. Michelis +1Soft Robotics Robot Morphology-Control Co-Design
Soft Robotics Lab, D-MAVT, ETH Zurich, Switzerland. · ETH AI Center, ETH Zurich, Switzerland.
Soft Pneumatic Grippers: Topology optimization, 3D-printing and Experimental validation
Nov 24, 2025Prabhat Kumar, Chandra Prakash, Josh Pinskier +2Topology Optimization Mechanism Design
Department Mechanical and Aerospace Engineering. India Institute of Technology Hyderbad, Telangana 502285, India · CSIRO Robotics, Pullenvale, QLD 4069, Australia · Faculty of Mechanical Engineering, Delft University of Technology, Mekelweg 2, Delft, 2628CD, Zuid-Holland, The Netherlands
FORTE: Task-Adaptive Force Capability Optimization for Mobile Manipulators
Sep 18, 2026Xiao Wang, Heng Zhang, Gokhan Solak +2Force-Controlled Robotic Manipulation Whole-Body Robotic Manipulation
HRI2 Lab, Istituto Italiano di Tecnologia, Genoa, 16163, Italy · Ph.D. program of national interest in Robotics and Intelligent Machines (DRIM) and University of Genova, Genoa, 16126, Italy · School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

Mar 6, 2026J/K move · Enter open · S save

A Unified Low-Dimensional Design Embedding for Joint Optimization of Shape, Material, and Actuation in Soft Robots

Vittorio Candiello, Manuel Mekkattu, Mike Y. Michelis, Robert K. Katzschmann

Soft Robotics Lab, D-MAVT, ETH Zurich, Switzerland. · ETH AI Center, ETH Zurich, Switzerland.

Soft Robotics Robot Morphology-Control Co-Design

Open paper AskPDF arXiv

Soft robots achieve functionality through tight coupling among geometry, material composition, and actuation. As a result, effective design optimization requires these three aspects to be considered jointly rather than in isolation. This coupling is computationally challenging: nonlinear large-deformation mechanics increase simulation cost, while contact, collision handling, and non-smooth state transitions limit the applicability of standard gradient-based approaches. We introduce a smooth, low-dimensional design embedding for soft robots that unifies shape morphing, multi-material distribution, and actuation within a single structured parameter space. Shape variation is modeled through continuous deformation maps of a reference geometry, while material properties are encoded as spatial fields. Both are constructed from shared basis functions. This representation enables expressive co-design while drastically reducing the dimensionality of the search space. In our experiments, we show that design expressiveness increases with the number of basis functions, unlike comparable neural network encodings whose representational capacity does not scale predictably with parameter count. We further show that joint co-optimization of shape, material, and actuation using our unified embedding consistently outperforms sequential strategies. All experiments are performed independently of the underlying simulator, confirming compatibility with black-box simulation pipelines. Across multiple dynamic tasks, the proposed embedding surpasses neural network and voxel-based baseline parameterizations while using significantly fewer design parameters. Together, these findings demonstrate that structuring the design space itself enables efficient co-design of soft robots.

Abstract

Soft Robotics

Explore similar work

A Unified Low-Dimensional Design Embedding for Joint Optimization of Shape, Material, and Actuation in Soft Robots

Soft Pneumatic Grippers: Topology optimization, 3D-printing and Experimental validation

FORTE: Task-Adaptive Force Capability Optimization for Mobile Manipulators